The Effect of Fog Offloading on the Energy Consumption of Computational Nodes

Sharifi, Ferdous; Hessabi, Shaahin; Rasaii, Ali

doi:10.1109/rtest56034.2022.9850011

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…This paper significantly extends our previous work [9] by (1) incorporating patient mobility into our model, creating a dynamic fog computing environment with varying data generation rates, and (2) conducting extensive experiments for the comprehensiveness of our findings.…”

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confidence: 69%

“…Among these challenges, in this work, we focused on computation offloading. Computation offloading enables IoT devices to offload computation tasks to fog or cloud servers and receive the results after the servers execute the tasks [8]. In this way, we can free end devices from heavy computing tasks, reduce their energy consumption, and significantly advance the completion time of tasks.…”

Section: Introductionmentioning

confidence: 99%

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On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

Sharifi,

Rasaii,

Pasdar

et al. 2023

Digital

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The emergence of fog computing has significantly enhanced real-time data processing by bringing computation resources closer to data sources. This adoption is very beneficial in the healthcare sector, where abundant time-sensitive processing tasks exist. Although such adoption is very promising, there is a challenge with the limited computational capacity of fog nodes. This challenge becomes even more critical when mobile IoT nodes enter the network, potentially increasing the network load. To address this challenge, this paper presents a framework that leverages a Many-to-One offloading (M2One) policy designed for modelling the dynamic nature and time-critical aspect of processing tasks in the healthcare domain. The framework benefits the multi-tier structure of the fog layer, making efficient use of the computing capacity of mobile fog nodes to enhance the overall computing capability of the fog network. Moreover, this framework accounts for mobile IoT nodes that generate an unpredictable volume of tasks at unpredictable intervals. Under the proposed policy, a first-tier fog node, called the coordinator fog node, efficiently manages all requests offloaded by the IoT nodes and allocates them to the fog nodes. It considers factors like the limited energy in the mobile nodes, the communication channel status, and low-latency demands to distribute requests among fog nodes and meet the stringent latency requirements of healthcare applications. Through extensive simulations in a healthcare scenario, the policy’s effectiveness showed an improvement of approximately 30% in average delay compared to cloud computing and a significant reduction in network usage.

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confidence: 69%

Section: Introductionmentioning

confidence: 99%

On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

Sharifi,

Rasaii,

Pasdar

et al. 2023

Digital

Self Cite

View full text Add to dashboard Cite

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“…Two cameras are initially attached per fog device which is increased in each succeeding scenario. The sensors installed in our evaluations are according to the strategy of Sharifi, Hessabi & Rasaii (2022) . The simulation model of one of each setup created in iFogSim for the evaluation of the fog paradigm and cloud computing paradigm is shown in Figs.…”

Section: Resultsmentioning

confidence: 99%

“…Each device in the system is accountable for the implementation of some application modules ( Hassan et al, 2020 ). Different amounts of resources are available at fog devices existing in heterogeneous fog computing environments ( Sharifi, Hessabi & Rasaii, 2022 ). These fog nodes provide a reduction in latency by processing information near the sensor nodes.…”

Section: Proposed Paradigm and Problem Formulationmentioning

confidence: 99%

Design of load-aware resource allocation for heterogeneous fog computing systems

Hassan,

Rehman,

Alsharabi

et al. 2024

PeerJ Computer Science

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The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.

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The Effect of Fog Offloading on the Energy Consumption of Computational Nodes

Cited by 2 publications

References 14 publications

On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

Design of load-aware resource allocation for heterogeneous fog computing systems

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